{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0175b99a-888b-42f6-b583-f9c5309a4c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b4743b-804c-4203-8ec5-e86fa29037a0",
   "metadata": {},
   "source": [
    "## Axes对象\n",
    "1. Axes对象是Matplotlib中绘制具体图形的容器\n",
    "2. plt的所有的图形绘制都是对Axes对应绘图方法的封装与调用：\n",
    "    * 例如，plt.plot调用的是Axes.plot；plt.scatter调用的是Axes.plot；plt.bar调用的是Axes.bar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cef02e-fc7e-451a-855d-9deaea860ff0",
   "metadata": {},
   "source": [
    "## 一、设置坐标轴的最大值和最小值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3fee8b2-351a-4fe1-8d37-029fff16da18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-3.0, 3.0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建画板\n",
    "fig = plt.figure()\n",
    "# 在fig上添加一张子图（Axes对象）\n",
    "ax1 = fig.add_subplot(111)\n",
    "# 设置子图的绘图数据，绘制折线图\n",
    "ax1.plot(np.random.randn(10))\n",
    "\n",
    "# 设置x轴的最大值和最小值\n",
    "ax1.set_xlim(-2, 10)\n",
    "# 设置y轴的最大值和最小值\n",
    "ax1.set_ylim(-3, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afea51e0-6c2a-480d-8a7d-f25e58071aaa",
   "metadata": {},
   "source": [
    "## 二、添加文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2219e46f-2b25-4bf8-8b4f-abc75efc61f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0, 'Hello')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建画板\n",
    "fig = plt.figure()\n",
    "# 在fig上添加一张子图（Axes对象）\n",
    "ax1 = fig.add_subplot(111)\n",
    "# 设置子图的绘图数据，绘制折线图\n",
    "ax1.plot(np.random.randn(10))\n",
    "\n",
    "# 添加简单的标准文本\n",
    "ax1.text(0, 0, \"Hello\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d163dcb-af65-46c5-90f7-3ebd544e5c96",
   "metadata": {},
   "source": [
    "## 三、绘制双Y轴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcea99dd-fd91-43d6-a1df-584666a913dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x106a2a050>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建画板\n",
    "fig = plt.figure()\n",
    "# 在fig上添加一张子图（Axes对象）\n",
    "ax1 = fig.add_subplot(111)\n",
    "# 设置子图ax1上的绘图数据，绘制柱状图\n",
    "ax1.bar(np.arange(0, 10), np.random.randn(10) * 100, label=\"temperature\")\n",
    "# 设置子图ax1的y轴标签\n",
    "ax1.set_ylabel(\"Temperature\")\n",
    "\n",
    "# 创建一个与ax1共享x轴对象的新子图ax2\n",
    "ax2 = ax1.twinx()\n",
    "# 设置子图ax2上的绘图数据，绘制折线图\n",
    "ax2.plot(np.arange(0, 10), np.random.randn(10) * 10, color=\"r\", label=\"rain\")\n",
    "# 设置子图ax2的y轴标签\n",
    "ax2.set_ylabel(\"Rain\")\n",
    "\n",
    "# 统一绘制fig上所有子图的图例\n",
    "fig.legend(ncol=2, loc=\"upper center\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
